Format

Send to

Choose Destination
J Am Soc Mass Spectrom. 2018 Aug;29(8):1721-1737. doi: 10.1007/s13361-018-1986-y. Epub 2018 Jun 5.

Rapid Classification and Identification of Multiple Microorganisms with Accurate Statistical Significance via High-Resolution Tandem Mass Spectrometry.

Author information

1
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.
2
Proteomics Core, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
3
Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA.
4
Department of Laboratory Medicine, Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA.
5
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA. yyu@ncbi.nlm.nih.gov.

Abstract

Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is complicating correct microbial identification even in a simple sample due to the large number of candidates present. To properly untwine candidate microbes in samples containing one or more microbes, one needs to go beyond apparent morphology or simple "fingerprinting"; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptide-centric representations of microbes to better separate them and by augmenting our earlier analysis method that yields accurate statistical significance. Here, we present an updated analysis workflow that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using 226 MS/MS publicly available data files (each containing from 2500 to nearly 100,000 MS/MS spectra) and 4000 additional MS/MS data files, that the updated workflow can correctly identify multiple microbes at the genus and often the species level for samples containing more than one microbe. We have also shown that the proposed workflow computes accurate statistical significances, i.e., E values for identified peptides and unified E values for identified microbes. Our updated analysis workflow MiCId, a freely available software for Microorganism Classification and Identification, is available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html . Graphical Abstract ᅟ.

KEYWORDS:

Mass; Microorganism classification; Pathogen identification; Proteomics; Spectrometry; Statistical significance

Supplemental Content

Full text links

Icon for Springer Icon for PubMed Central
Loading ...
Support Center